A neural network may comprise a plurality of numerical computational machines, each of which may utilize a large number of multiply-accumulate operations. The multiply-accumulate operations typically require a set of numerical coefficients, and the number of numerical coefficients may be stored in a centralized list and communicated to one or more neural network engines which use them.
When the list of coefficients is large, the time required to transmit the coefficients to all of the neural networks is governed by the amount of data to transmit and the data rate of channel. Additionally, it is known that numerical data tends to be difficult to compress using traditional compression algorithms, as the data tends to have high entropy.
For this reason, it is desired to provide a method and apparatus for transmission and reception of numerical data in the form of coefficients across a network to one or more neural network in need of accurate coefficients after decompression and decoding.